Run a workflow via it's YAML specification.

Attempts to run a single assessment (lWorkflow) using shared data (lData) and metadata (lMapping). Calls RunStep for each item in lWorkflow$workflow and saves the results to lWorkflow.

Usage

RunWorkflow(
  lWorkflow,
  lData = NULL,
  lConfig = NULL,
  bReturnResult = TRUE,
  bKeepInputData = TRUE
)

Arguments

lWorkflow

list A named list of metadata defining how the workflow should be run.

lData

list A named list of domain-level data frames.

lConfig

list A configuration object with two methods:

LoadData: A function that loads data specified in lWorkflow$spec.
SaveData: A function that saves data returned by the last step in lWorkflow$steps.

bReturnResult

boolean should only the result from the last step (lResults) be returned? If false, the full workflow (including lResults) is returned. Default is TRUE.

bKeepInputData

boolean should the input data be included in lData after the workflow is run? Only relevant when bReturnResult is FALSE. Default is TRUE.

Value

Object containing the results of the workflow's last step (if bLastResult is TRUE) or the full workflow object (if bReturnResults is TRUE) or the full workflow object (if bReturnResults is FALSE).

list contains just lData if bReturnData is TRUE, otherwise returns the full lWorkflow object.

Examples

# Generate mapped input data to metric workflow.
lMappingWorkflows <- MakeWorkflowList(
  strNames = c("AE", "SUBJ"),
  strPath = "example_workflow/1_mappings",
  strPackage = "gsm.core",
  bExact = TRUE
)
lRawData <- list(
  Raw_SUBJ = gsm.core::lSource$Raw_SUBJ,
  Raw_AE = gsm.core::lSource$Raw_AE
)

lMappedData <- RunWorkflows(
  lMappingWorkflows,
  lRawData
)
#> 
#> ── Running 2 Workflows ─────────────────────────────────────────────────────────
#> 
#> ── Initializing `Mapped_AE` Workflow ───────────────────────────────────────────
#> 
#> ── Checking data against spec 
#> → All 1 data.frame(s) in the spec are present in the data: Raw_AE
#> → All specified columns in Raw_AE are in the expected format
#> → All 2 specified column(s) in the spec are present in the data: Raw_AE$subjid, Raw_AE$aeser
#> 
#> ── Workflow Step 1 of 1: `=` ──
#> 
#> ── Evaluating 2 parameter(s) for `=` 
#> ℹ lhs = Mapped_AE: No matching data found. Passing 'Mapped_AE' as a string.
#> ✔ rhs = Raw_AE: Passing lData$Raw_AE.
#> 
#> ── Calling `=` 
#> 
#> ── 3000x11 data.frame saved as `lData$Mapped_AE`. 
#> 
#> ── Returning results from final step: 3000x11 data.frame`. ──
#> 
#> ── Completed `Mapped_AE` Workflow ──────────────────────────────────────────────
#> 
#> ── Initializing `Mapped_SUBJ` Workflow ─────────────────────────────────────────
#> 
#> ── Checking data against spec 
#> → All 1 data.frame(s) in the spec are present in the data: Raw_SUBJ
#> → All specified columns in Raw_SUBJ are in the expected format
#> → All 7 specified column(s) in the spec are present in the data: Raw_SUBJ$studyid, Raw_SUBJ$invid, Raw_SUBJ$country, Raw_SUBJ$subjid, Raw_SUBJ$subject_nsv, Raw_SUBJ$enrollyn, Raw_SUBJ$timeonstudy
#> 
#> ── Workflow Step 1 of 1: `gsm.core::RunQuery` ──
#> 
#> ── Evaluating 2 parameter(s) for `gsm.core::RunQuery` 
#> ✔ df = Raw_SUBJ: Passing lData$Raw_SUBJ.
#> ℹ strQuery = SELECT * FROM df WHERE enrollyn == 'Y': No matching data found. Passing 'SELECT * FROM df WHERE enrollyn == 'Y'' as a string.
#> 
#> ── Calling `gsm.core::RunQuery` 
#> Creating a new temporary DuckDB connection.
#> ✔ SQL Query complete: 763 rows returned.
#> Disconnected from temporary DuckDB connection.
#> 
#> ── 763x15 data.frame saved as `lData$Mapped_SUBJ`. 
#> 
#> ── Returning results from final step: 763x15 data.frame`. ──
#> 
#> ── Completed `Mapped_SUBJ` Workflow ────────────────────────────────────────────

# Run the metric workflow.
lMetricWorkflow <- MakeWorkflowList(
  strPath = "example_workflow/2_metrics",
  strNames = c("kri0001", "kri0002"),
  strPackage = "gsm.core"
)$kri0001
lMetricOutput <- RunWorkflow(
  lMetricWorkflow,
  lMappedData
)
#> 
#> ── Initializing `Analysis_kri0001` Workflow ────────────────────────────────────
#> 
#> ── Checking data against spec 
#> → All 2 data.frame(s) in the spec are present in the data: Mapped_AE, Mapped_SUBJ
#> → All specified columns in Mapped_AE are in the expected format
#> → All specified columns in Mapped_SUBJ are in the expected format
#> → All 4 specified column(s) in the spec are present in the data: Mapped_AE$subjid, Mapped_SUBJ$subjid, Mapped_SUBJ$invid, Mapped_SUBJ$timeonstudy
#> 
#> ── Workflow Step 1 of 7: `gsm.core::ParseThreshold` ──
#> 
#> ── Evaluating 1 parameter(s) for `gsm.core::ParseThreshold` 
#> ✔ strThreshold = Threshold: Passing lMeta$Threshold.
#> 
#> ── Calling `gsm.core::ParseThreshold` 
#> Parsed -2,-1,2,3 to numeric vector: -2, -1, 2, 3
#> 
#> ── double of length 4 saved as `lData$vThreshold`. 
#> 
#> ── Workflow Step 2 of 7: `gsm.core::Input_Rate` ──
#> 
#> ── Evaluating 9 parameter(s) for `gsm.core::Input_Rate` 
#> ✔ dfSubjects = Mapped_SUBJ: Passing lData$Mapped_SUBJ.
#> ✔ dfNumerator = Mapped_AE: Passing lData$Mapped_AE.
#> ✔ dfDenominator = Mapped_SUBJ: Passing lData$Mapped_SUBJ.
#> ℹ strSubjectCol = subjid: No matching data found. Passing 'subjid' as a string.
#> ℹ strGroupCol = invid: No matching data found. Passing 'invid' as a string.
#> ✔ strGroupLevel = GroupLevel: Passing lMeta$GroupLevel.
#> ℹ strNumeratorMethod = Count: No matching data found. Passing 'Count' as a string.
#> ℹ strDenominatorMethod = Sum: No matching data found. Passing 'Sum' as a string.
#> ℹ strDenominatorCol = timeonstudy: No matching data found. Passing 'timeonstudy' as a string.
#> 
#> ── Calling `gsm.core::Input_Rate` 
#> 
#> ── 763x6 data.frame saved as `lData$Analysis_Input`. 
#> 
#> ── Workflow Step 3 of 7: `gsm.core::Transform_Rate` ──
#> 
#> ── Evaluating 1 parameter(s) for `gsm.core::Transform_Rate` 
#> ✔ dfInput = Analysis_Input: Passing lData$Analysis_Input.
#> 
#> ── Calling `gsm.core::Transform_Rate` 
#> 
#> ── 145x5 data.frame saved as `lData$Analysis_Transformed`. 
#> 
#> ── Workflow Step 4 of 7: `gsm.core::Analyze_NormalApprox` ──
#> 
#> ── Evaluating 2 parameter(s) for `gsm.core::Analyze_NormalApprox` 
#> ✔ dfTransformed = Analysis_Transformed: Passing lData$Analysis_Transformed.
#> ✔ strType = AnalysisType: Passing lMeta$AnalysisType.
#> 
#> ── Calling `gsm.core::Analyze_NormalApprox` 
#> `OverallMetric`, `Factor`, and `Score` columns created from normal
#> approximation.
#> 
#> ── 145x8 data.frame saved as `lData$Analysis_Analyzed`. 
#> 
#> ── Workflow Step 5 of 7: `gsm.core::Flag` ──
#> 
#> ── Evaluating 4 parameter(s) for `gsm.core::Flag` 
#> ✔ dfAnalyzed = Analysis_Analyzed: Passing lData$Analysis_Analyzed.
#> ✔ vThreshold = vThreshold: Passing lData$vThreshold.
#> ✔ nAccrualThreshold = AccrualThreshold: Passing lMeta$AccrualThreshold.
#> ✔ strAccrualMetric = AccrualMetric: Passing lMeta$AccrualMetric.
#> 
#> ── Calling `gsm.core::Flag` 
#> ℹ 20 Group(s) have insufficient sample size due to KRI denominator less than 30: 0X4185, 0X5579, 0X8592, 0X7147, 0X5182, 0X7844, 0X3556, 0X9364, 0X2508, 0X3857, 0X3214, 0X1800, 0X155, 0X5503, 0X439, 0X6041, 0X3777, 0X8547, 0X3565, 0X283
#> These group(s) will not have KRI score and flag summarized.
#> ℹ Sorted dfFlagged using custom Flag order: 2.Sorted dfFlagged using custom Flag order: -2.Sorted dfFlagged using custom Flag order: 1.Sorted dfFlagged using custom Flag order: -1.Sorted dfFlagged using custom Flag order: 0.
#> 
#> ── 145x9 data.frame saved as `lData$Analysis_Flagged`. 
#> 
#> ── Workflow Step 6 of 7: `gsm.core::Summarize` ──
#> 
#> ── Evaluating 1 parameter(s) for `gsm.core::Summarize` 
#> ✔ dfFlagged = Analysis_Flagged: Passing lData$Analysis_Flagged.
#> 
#> ── Calling `gsm.core::Summarize` 
#> 
#> ── 145x7 data.frame saved as `lData$Analysis_Summary`. 
#> 
#> ── Workflow Step 7 of 7: `list` ──
#> 
#> ── Evaluating 6 parameter(s) for `list` 
#> ✔ ID = ID: Passing lMeta$ID.
#> ✔ Analysis_Input = Analysis_Input: Passing lData$Analysis_Input.
#> ✔ Analysis_Transformed = Analysis_Transformed: Passing lData$Analysis_Transformed.
#> ✔ Analysis_Analyzed = Analysis_Analyzed: Passing lData$Analysis_Analyzed.
#> ✔ Analysis_Flagged = Analysis_Flagged: Passing lData$Analysis_Flagged.
#> ✔ Analysis_Summary = Analysis_Summary: Passing lData$Analysis_Summary.
#> 
#> ── Calling `list` 
#> 
#> ── list of length 6 saved as `lData$lAnalysis`. 
#> 
#> ── Returning results from final step: list of length 6`. ──
#> 
#> ── Completed `Analysis_kri0001` Workflow ───────────────────────────────────────